Privacy-preserving statistical quantitative rules mining

Authors:
Weiwei Jing;Liusheng Huang;Yifei Yao;Weijiang Xu
Affiliations:
USTC, East Campus USTC, Hefei, PRC;USTC, East Campus USTC, Hefei, PRC;USTC, East Campus USTC, Hefei, PRC;USTC, East Campus USTC, Hefei, PRC
Venue:
Proceedings of the 2nd international conference on Scalable information systems
Year:
2007

Citing 3
Cited 0

On the discovery of significant statistical quantitative rules

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data

IEEE Transactions on Knowledge and Data Engineering
An Algorithm for Privacy-Preserving Quantitative Association Rules Mining

DASC '06 Proceedings of the 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper considers the problem of mining Statistical Quantitative rules (SQ rules) without revealing the private information of parties who compute jointly and share distributed data. Based on several basic tools for Privacy-Preserving Data Mining (PPDM), including secure sum, secure mean and secure frequent itemsets, this paper presents two algorithms to accomplish privacy-preserving SQ rules mining over horizontally partitioned data. One is to securely compute confidence intervals for testing the significance of rules; the other is to securely discover SQ rules.